使用旋转森林算法预测乳腺癌,并找到影响因素.
Prosenjit Das1, Proshenjit Sarker1, Jun-Jiat Tiang2
1Electronics and Communication Engineering Discipline, Khulna University, Khulna 9208, Bangladesh.
Bioengineering (Basel, Switzerland)
|October 29, 2025
概括
这项研究使用机器学习来分类乳腺癌 (BC) 病例. 优化旋转森林的硬投票策略实现了85.71%的准确性,将BMI和葡萄糖确定为关键预测特征.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的机器学习
- 在瘤学瘤学.
背景情况:
- 乳腺癌 (BC) 是一个重要的全球健康问题,主要影响女性.
- 早期检测和准确的分类对于改善患者的治疗结果至关重要.
- 机器学习为提高BC诊断和风险评估提供了有希望的途径.
研究的目的:
- 使用机器学习对乳腺癌 (BC) 和非BC病例进行分类.
- 优化分类器性能和特征选择,以提高诊断准确度.
- 通过反事实解释来确定BC分类的关键影响性特征.
主要方法:
- 使用了旋转森林分类器,其超参数由Optuna优化器优化.
- 采用顺序前向选择,顺序后向选择和详尽的特征选择来进行特征选择.
- 开发了一个集体模型,用于软硬的投票策略进行分类.
主要成果:
- 硬投票策略取得了优异的表现,准确率为85.71%,F1得分为83.87%,精度为92.85%,回忆率为76.47%.
- 软投票策略的准确率为80.00%,F1得分为77.42%,精度为85.71%,回忆率为70.59%.
- 不同的反事实解释将BMI和葡萄糖确定为具有高度影响力的特征,而HOMA,阿迪波内克丁和Resistin则显示出最小的影响.
结论:
- 优化的旋转森林分类器与硬投票组合显著提高了乳腺癌分类准确度.
- 体重指数和葡萄糖水平是预测乳腺癌的关键指标,突出了它们在临床评估中的重要性.
- 特性选择和超参数优化对于最大限度地提高机器学习模型在瘤学中的有效性至关重要.
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